【Flink】FlinkSQL元数据验证

转x33g5p2x  于2022-02-07 转载在 Flink  
字(14.2k)|赞(0)|评价(0)|浏览(601)

1.概述

转载：FlinkSQL元数据验证

Flink1.9以后引入CatalogManager来管理Catalog和CatalogBaseTable，在执行DDL语句时将表信息封装为CatalogBaseTable存储在CatalogManager中。同时扩展了calcite的Schema接口使得Calcite在Validate阶段能够读取CatalogManager中的表信息。

2.CatalogTable写入

通过执行DDL语句，查看BlinkPlanner如何解析DDL语句，并存储到CatalogManamer中，重点查看如何解析protime as PROCTIME() 计算列的。

CREATE TABLE user_address (
     userId BIGINT,
     addressInfo VARCHAR,
     proctime AS PROCTIME()
) WITH (
  'connector' = 'kafka',
  'properties.bootstrap.servers' = 'localhost:9092',
  'topic' = 'tp02',
  'format' = 'json',
  'scan.startup.mode' = 'latest-offset'
 )

执行createCatalogTable调用链路。

org.apache.flink.table.api.internal.TableEnvironmentImpl#executeSql
    org.apache.flink.table.planner.delegation.ParserImpl#parse
        org.apache.flink.table.planner.operations.SqlToOperationConverter#convert
            org.apache.flink.table.planner.operations.SqlCreateTableConverter#convertCreateTable
                // 从SqlCreateTable语句中解析出CatalogTable
                org.apache.flink.table.planner.operations.SqlCreateTableConverter#createCatalogTable

从SqlCreateTable中提取TableSchema、表分区、主键、注释等信息，从而构建CatalogTableImpl。

private CatalogTable createCatalogTable(SqlCreateTable sqlCreateTable) {
        final TableSchema sourceTableSchema;
        final List<String> sourcePartitionKeys;
        final List<SqlTableLike.SqlTableLikeOption> likeOptions;
        final Map<String, String> sourceProperties;
        // 处理 create table like ...
        if (sqlCreateTable.getTableLike().isPresent()) {
            SqlTableLike sqlTableLike = sqlCreateTable.getTableLike().get();
            CatalogTable table = lookupLikeSourceTable(sqlTableLike);
            sourceTableSchema = table.getSchema();
            sourcePartitionKeys = table.getPartitionKeys();
            likeOptions = sqlTableLike.getOptions();
            sourceProperties = table.getProperties();
        } else {
            sourceTableSchema = TableSchema.builder().build();
            sourcePartitionKeys = Collections.emptyList();
            likeOptions = Collections.emptyList();
            sourceProperties = Collections.emptyMap();
        }
        //  处理SqlTableLike中的选项，INCLUDING ALL、OVERWRITING OPTIONS、EXCLUDING PARTITIONS等
        Map<SqlTableLike.FeatureOption, SqlTableLike.MergingStrategy> mergingStrategies =
            mergeTableLikeUtil.computeMergingStrategies(likeOptions);

        Map<String, String> mergedOptions = mergeOptions(sqlCreateTable, sourceProperties, mergingStrategies);
        //  提取主键
        Optional<SqlTableConstraint> primaryKey = sqlCreateTable.getFullConstraints()
            .stream()
            .filter(SqlTableConstraint::isPrimaryKey)
            .findAny();

        // 获取TableSchema
        TableSchema mergedSchema = mergeTableLikeUtil.mergeTables(
            mergingStrategies,
            sourceTableSchema,  //  非create table like 语句，sourceTableSchema为null。
            sqlCreateTable.getColumnList().getList(),
            sqlCreateTable.getWatermark().map(Collections::singletonList).orElseGet(Collections::emptyList),
            primaryKey.orElse(null)
        );

        // 表分区
        List<String> partitionKeys = mergePartitions(
            sourcePartitionKeys,
            sqlCreateTable.getPartitionKeyList(),
            mergingStrategies
        );
        verifyPartitioningColumnsExist(mergedSchema, partitionKeys);
        //  注释
        String tableComment = sqlCreateTable.getComment()
            .map(comment -> comment.getNlsString().getValue())
            .orElse(null);

        return new CatalogTableImpl(mergedSchema,
            partitionKeys,
            mergedOptions,
            tableComment);
    }

在提取TableSchema时，会将Calcite中的列的类型转换为Flink内部的数据类型。如果包含了计算列，例如procime()则会对该表达式进行验证，FlinkSqlOperatorTable类中包含了FlinkSQL的所有内置函数。

/**
     * Function used to access a processing time attribute.
     */
    public static final SqlFunction PROCTIME =
        new CalciteSqlFunction(
            "PROCTIME",
            SqlKind.OTHER_FUNCTION,
            PROCTIME_TYPE_INFERENCE,
            null,
            OperandTypes.NILADIC,
            SqlFunctionCategory.TIMEDATE,
            false
        );

TableColumn生成过程：

  • 将非计算列进行类型转换，并存储到physicalFieldNamesToTypes集合。
  • 对通过表达式生成的计算列，进行存在性验证，并返回该函数对应的RelDataType。
  • 将字段的RelDataType转换为LogicalType，再转换为DataType，并构建为TableColumn。需要具体查看不同类型系统的区别。
private void appendDerivedColumns(
                Map<FeatureOption, MergingStrategy> mergingStrategies,
                List<SqlNode> derivedColumns) {
// 非计算列进行数据转换，存储到physicalFieldNamesToTypes
            collectPhysicalFieldsTypes(derivedColumns);

            for (SqlNode derivedColumn : derivedColumns) {
                final SqlTableColumn tableColumn = (SqlTableColumn) derivedColumn;
                final TableColumn column;
                if (tableColumn.isGenerated()) {
                    String fieldName = tableColumn.getName().toString();
                    //验证表达式，例如：proctime()函数 是否在FlinkSqlOperatorTable内注册
                    SqlNode validatedExpr = sqlValidator.validateParameterizedExpression(
                        tableColumn.getExpr().get(),
                        physicalFieldNamesToTypes);
            
//  验证返回类型：proctime() 对应的RelDataType 为Flink扩展的TimeIndicatorRelDataType  
                    final RelDataType validatedType = sqlValidator.getValidatedNodeType(validatedExpr);
                    column = TableColumn.of(
                        fieldName,
//   RelDataType--->LogicalType--->DataType
                        fromLogicalToDataType(toLogicalType(validatedType)),
                        escapeExpressions.apply(validatedExpr));

                    computedFieldNamesToTypes.put(fieldName, validatedType);
                } else {
            // 非计算列转换为Flink 内部的数据类型
                    String name = tableColumn.getName().getSimple();
//    RelDataType --> LogicalType --> DataType
                    LogicalType logicalType = FlinkTypeFactory.toLogicalType(physicalFieldNamesToTypes.get(name));
                    column = TableColumn.of(name, TypeConversions.fromLogicalToDataType(logicalType));
                }
                columns.put(column.getName(), column);
            }
        }

计算列proctime信息，需要看下proctime Function定义时绑定的类型。

3.Validate 读取元数据

FlinkSchema包含三个子类分别为：CatalogManagerCalciteSchema，CatalogCalciteSchema，DatabaseCalciteSchema。在Calcite进行Validate时，通过调用重写的getSubSchema方法依次获取Catalog、Database信息，最终从catalogManager中获取对应的Table信息。
通过创建自定义Schema，查看Calcite获取表Schema信息。测试用例

// 当前Scheam包含了USERS、JOBS两张表信息，
public class CatalogManagerCalciteSchema implements Schema {
    static Map<String, Table> TABLES = Maps.newHashMap();

    static {
        TABLES.put("USERS", new AbstractTable() { //note: add a table
            @Override
            public RelDataType getRowType(final RelDataTypeFactory typeFactory) {
                RelDataTypeFactory.Builder builder = typeFactory.builder();
                builder.add("ID", new BasicSqlType(new RelDataTypeSystemImpl() {
                }, SqlTypeName.INTEGER));
                builder.add("NAME", new BasicSqlType(new RelDataTypeSystemImpl() {
                }, SqlTypeName.CHAR));
                builder.add("AGE", new BasicSqlType(new RelDataTypeSystemImpl() {
                }, SqlTypeName.INTEGER));
                return builder.build();
            }
        });

        TABLES.put("JOBS", new AbstractTable() {
            @Override
            public RelDataType getRowType(final RelDataTypeFactory typeFactory) {
                RelDataTypeFactory.Builder builder = typeFactory.builder();

                builder.add("ID", new BasicSqlType(new RelDataTypeSystemImpl() {
                }, SqlTypeName.INTEGER));
                builder.add("NAME", new BasicSqlType(new RelDataTypeSystemImpl() {
                }, SqlTypeName.CHAR));
                builder.add("COMPANY", new BasicSqlType(new RelDataTypeSystemImpl() {
                }, SqlTypeName.CHAR));
                return builder.build();
            }
        });
    }

    @Override
    public Table getTable(String name) {
        return TABLES.get(name);
    }

    @Override
    public Set<String> getTableNames() {
        return null;
    }

    @Override
    public RelProtoDataType getType(String name) {
        return null;
    }

    @Override
    public Set<String> getTypeNames() {
        return null;
    }

    @Override
    public Collection<Function> getFunctions(String name) {
        return null;
    }

    @Override
    public Set<String> getFunctionNames() {
        return Collections.emptySet();
    }

    @Override
    public Schema getSubSchema(String name) {
        return null;
    }

    @Override
    public Set<String> getSubSchemaNames() {
        return null;
    }

    @Override
    public Expression getExpression(SchemaPlus parentSchema, String name) {
        return null;
    }

    @Override
    public boolean isMutable() {
        return false;
    }

    @Override
    public Schema snapshot(SchemaVersion version) {
        return this;
    }
}

案例

public static void main(String[] args) throws SqlParseException {
        // CatalogManagerCalciteSchema是自定义的，非Flink内部的
        CalciteSchema rootSchema =
                CalciteSchemaBuilder.asRootSchema(new CatalogManagerCalciteSchema());
        SchemaPlus schemaPlus = rootSchema.plus();
 
        SqlTypeFactoryImpl factory = new SqlTypeFactoryImpl(RelDataTypeSystem.DEFAULT);
        //  创建CalciteCatalogReader在rel阶段时从SimpleCalciteSchema中读取元数据
        CalciteCatalogReader calciteCatalogReader = new CalciteCatalogReader(
                CalciteSchema.from(schemaPlus),
                CalciteSchema.from(schemaPlus).path(null),
                factory,
                new CalciteConnectionConfigImpl(new Properties()));
              
        String sql = "select u.id as user_id, u.name as user_name, j.company as user_company, u.age as user_age \n"
                + "from users u join jobs j on u.name=j.name";

        SqlParser parser = SqlParser.create(sql, SqlParser.Config.DEFAULT);
        SqlNode sqlNode = parser.parseStmt();

        SqlValidator
                validator = SqlValidatorUtil.newValidator(SqlStdOperatorTable.instance(), calciteCatalogReader, factory,
                SqlConformanceEnum.DEFAULT);
        SqlNode validateSqlNode = validator.validate(sqlNode);
        System.out.println(validateSqlNode);
    }

Calcite通过Validate访问CatalogManagerCalciteSchema的调用链路，getSubSchema为null时代表没有子的Schema信息，则从当前Scheam读取Table信息。

Flink定义了三级Schema，通过调用getSubSchema从CatalogManager中读取Catalog、Database、Table。具体调用需要参考：org.apache.calcite.sql.validate.EmptyScope#resolve_。

CatalogManagerCalciteSchema#getSubSchemaNames：通过表名中的catalog信息，从catalogManager中获取CatalogSchema。

@Override
    public Schema getSubSchema(String name) {
        if (catalogManager.schemaExists(name)) {
            return new CatalogCalciteSchema(name, catalogManager, isStreamingMode);
        } else {
            return null;
        }
    }

CatalogCalciteSchema#getSubSchemaNames：通过表名中的database信息，从catalogManager中获取DatabaseSchema。

/**
     * Look up a sub-schema (database) by the given sub-schema name.
     *
     * @param schemaName name of sub-schema to look up
     * @return the sub-schema with a given database name, or null
     */
    @Override
    public Schema getSubSchema(String schemaName) {
        if (catalogManager.schemaExists(catalogName, schemaName)) {
            return new DatabaseCalciteSchema(schemaName, catalogName, catalogManager, isStreamingMode);
        } else {
            return null;
        }
    }

DatabaseSchema没有SubScheam，则从当前Schema中获取Table信息。

public Table getTable(String tableName) {
        ObjectIdentifier identifier = ObjectIdentifier.of(catalogName, databaseName, tableName);
        return catalogManager.getTable(identifier)
            .map(result -> {
                CatalogBaseTable table = result.getTable();
                FlinkStatistic statistic = getStatistic(result.isTemporary(), table, identifier);
                return new CatalogSchemaTable(
                    identifier,
                    result,
                    statistic,
                    catalogManager.getCatalog(catalogName).orElseThrow(IllegalStateException::new),
                    isStreamingMode);
            })
            .orElse(null);
    }

4.Proctime 字段验证

flinkSQL在validate读取Table schema时，会对计算列rowtime、proctime类型进行转换，转换为calcite能识别的RelDataType类型。 先列举下计算列类型转换的代码。

## CatalogManager
public Optional<TableLookupResult> getTable(ObjectIdentifier objectIdentifier) {
        CatalogBaseTable temporaryTable = temporaryTables.get(objectIdentifier);
        if (temporaryTable != null) {
            TableSchema resolvedSchema = resolveTableSchema(temporaryTable);
            return Optional.of(TableLookupResult.temporary(temporaryTable, resolvedSchema));
        } else {
            return getPermanentTable(objectIdentifier);
        }
    }
## org.apache.flink.table.api.internal.CatalogTableSchemaResolver#resolve

    /**
     * Resolve the computed column's type for the given schema.
     *
     * @param tableSchema Table schema to derive table field names and data types
     * @return the resolved TableSchema
     */
    public TableSchema resolve(TableSchema tableSchema) {
        final String rowtime;
        String[] fieldNames = tableSchema.getFieldNames();
        DataType[] fieldTypes = tableSchema.getFieldDataTypes();

        TableSchema.Builder builder = TableSchema.builder();
        for (int i = 0; i < tableSchema.getFieldCount(); ++i) {
            TableColumn tableColumn = tableSchema.getTableColumns().get(i);
            DataType fieldType = fieldTypes[i];

            if (tableColumn.isGenerated()) {
        //  通过获取计算列的表达式，提取对应的DataType
                fieldType = resolveExpressionDataType(tableColumn.getExpr().get(), tableSchema);
                if (isProctime(fieldType)) {
                    if (fieldNames[i].equals(rowtime)) {
                        throw new TableException("Watermark can not be defined for a processing time attribute column.");
                    }
                }
            }
                ......

            if (tableColumn.isGenerated()) {
                builder.field(fieldNames[i], fieldType, tableColumn.getExpr().get());
            } else {
                builder.field(fieldNames[i], fieldType);
            }
        }
        tableSchema.getWatermarkSpecs().forEach(builder::watermark);
        tableSchema.getPrimaryKey().ifPresent(
                pk -> builder.primaryKey(pk.getName(), pk.getColumns().toArray(new String[0])));
        return builder.build();
    }
# org.apache.flink.table.api.internal.CatalogTableSchemaResolver#resolveExpressionDataType
  
  private DataType resolveExpressionDataType(String expr, TableSchema tableSchema) {
        ResolvedExpression resolvedExpr = parser.parseSqlExpression(expr, tableSchema);
        if (resolvedExpr == null) {
            throw new ValidationException("Could not resolve field expression: " + expr);
        }
        return resolvedExpr.getOutputDataType();
    }
  
  # org.apache.flink.table.planner.delegation.ParserImpl#parseSqlExpression
  
  public ResolvedExpression parseSqlExpression(String sqlExpression, TableSchema inputSchema) {
        SqlExprToRexConverter sqlExprToRexConverter = sqlExprToRexConverterCreator.apply(inputSchema);
        RexNode rexNode = sqlExprToRexConverter.convertToRexNode(sqlExpression);
        //    [[RelDataType]]  ---->  [[LogicalType]]
        LogicalType logicalType = FlinkTypeFactory.toLogicalType(rexNode.getType());
        return new RexNodeExpression(rexNode, TypeConversions.fromLogicalToDataType(logicalType));
    }
  
  # org.apache.flink.table.planner.calcite.SqlExprToRexConverterImpl#convertToRexNodes
  
  public RexNode[] convertToRexNodes(String[] exprs) {
        //  通过构造临时表查询，获取RexNode
        String query = String.format(QUERY_FORMAT, String.join(",", exprs));
        SqlNode parsed = planner.parser().parse(query);
        SqlNode validated = planner.validate(parsed);
    // 转换为relNode
        RelNode rel = planner.rel(validated).rel;
        // The plan should in the following tree
        // LogicalProject
        // +- TableScan
        if (rel instanceof LogicalProject
            && rel.getInput(0) != null
            && rel.getInput(0) instanceof TableScan) {
            return ((LogicalProject) rel).getProjects().toArray(new RexNode[0]);
        } else {
            throw new IllegalStateException("The root RelNode should be LogicalProject, but is " + rel.toString());
        }
    }

proctime()类型提取大致流程：

  1. 包含计算列则将建表语句中TableSchema注册为一张表temp_table。
  2. 根据建表中的计算列的表达式，例如proctime()，构建临时查询语句select proctime() from temp_table，proctime() 为Flink 内置函数。
  3. 对该查询语句进行validate，并转换RelNode，从RelNode提取行表达式RexNode。
  4. 从RexNode提取proctime() 对应的RelDataType，最终转换为DataType。

[FLINK-18378]之前对计算列的处理流程。根据proctime、rowtime单独做了区分。
疑问：在DDL语句中已经将proctime转换为DataType，在validate获取Table schema是直接拿fieldType即可，为什么还要做一次解析。

for (int i = 0; i < tableSchema.getFieldCount(); ++i) {
            TableColumn tableColumn = tableSchema.getTableColumns().get(i);
            DataType fieldType = fieldTypes[i];
            if (tableColumn.isGenerated() && isProctimeType(tableColumn.getExpr().get(), tableSchema)) {
                if (fieldNames[i].equals(rowtime)) {
                    throw new TableException("Watermark can not be defined for a processing time attribute column.");
                }
                TimestampType originalType = (TimestampType) fieldType.getLogicalType();
                LogicalType proctimeType = new TimestampType(
                        originalType.isNullable(),
                        TimestampKind.PROCTIME,
                        originalType.getPrecision());
                fieldType = TypeConversions.fromLogicalToDataType(proctimeType);
            } else if (isStreamingMode && fieldNames[i].equals(rowtime)) {
                TimestampType originalType = (TimestampType) fieldType.getLogicalType();
                LogicalType rowtimeType = new TimestampType(
                        originalType.isNullable(),
                        TimestampKind.ROWTIME,
                        originalType.getPrecision());
                fieldType = TypeConversions.fromLogicalToDataType(rowtimeType);
            }
            if (tableColumn.isGenerated()) {
                builder.field(fieldNames[i], fieldType, tableColumn.getExpr().get());
            } else {
                builder.field(fieldNames[i], fieldType);
            }
        }

版权说明 : 本文为转载文章, 版权归原作者所有 版权申明

原文链接 : https://blog.csdn.net/qq_21383435/article/details/122769008

内容来源于网络，如有侵权，请联系作者删除！

flinkkafkaapache

相关文章

    查看更多
    微信公众号

    热门标签

    更多
    JavaquerypythonNode开发语言requestUtil数据库Table后端算法LoggerMessageElementParser

    最新文章

    更多
    • 蜀ICP备13028337号-1 大数据知识库 https://www.saoniuhuo.com © All rights reserved
    • 本站内容来源互联网,如果侵犯您的权益请联系我们删除, 联系方式：448109455@qq.com